# It is possible to solve a problem with continuous action spaces and no states with reinforcement learning?

I want to use Reinforcement Learning to optimize the distribution of energy for a peak shaving problem given by a thermodynamical simulation. However, I am not sure how to proceed as the action space is the only thing that really matters, in this sense:

• The action space is a $$288 \times 66$$ matrix of real numbers between $$0$$ and $$1$$. The output of the simulation and therefore my reward depend solely on the distribution of this matrix.

• The state space is therefore absent, as the only thing that matters is the matrix on which I have total control. At this stage of the simulation, no other variables are taken into consideration.

I am not sure if this problem falls into the tabular RL or it requires approximation. In this case, I was thinking about using a policy gradient algorithm for figuring out the best distribution of the $$288 \times 66$$ matrix. However, I do not know how to behave with the "absence" of the state space. Instead of a tuple $$\langle s,a,r,s' \rangle$$, I would just have $$\langle a, r \rangle$$, is this even an RL-approachable problem? If not, how can I reshape it to make it solvable with RL techniques?

To chose between exploration and exploitation moves, MAB agents adopt a strategy. The simplest one would probably be $$\epsilon$$-greedy which agent chooses the most rewarding actions most of the time (1-$$\epsilon$$ probability) or randomly selects an action ($$\epsilon$$ probability).